k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Protecting privacy in data release
Foundations of security analysis and design VI
Protecting privacy of sensitive value distributions in data release
STM'10 Proceedings of the 6th international conference on Security and trust management
Modeling and preventing inferences from sensitive value distributions in data release
Journal of Computer Security - STM'10
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Recently many schemes, including k-anonymity [8], l-diversity [6] and t-closeness [5] have been introduced for preserving individual privacy when publishing database tables. Furthermore k-anonymity and l-diversity have been shown to have weaknesses. In this paper, we show that t-closeness also has limitations, more specifically we argue that: i) choosing the correct value for t is difficult, ii) t-closeness does not allow some values of sensitive attributes to be more sensitive than other values, and iii) to prevent certain types of privacy leaks t must be set to such a small value that it produces low-quality published data. In this paper we propose a new privacy metric,(αi, βi)-closeness, that mitigates these problems. We also show how to calculate an optimal release table (in the full domain model) that satisfies (αi, βi)-closeness and we present experimental results that show that the data quality provided by 9αi, β;i),-closeness is higher than t-closeness, k-anonymity, and l-diversity while achieving the same privacy goals.